CN116703637A - Digital control system for wheat planting in northern arid region and application method thereof - Google Patents
Digital control system for wheat planting in northern arid region and application method thereof Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 claims abstract description 33
- 238000012544 monitoring process Methods 0.000 claims abstract description 30
- 238000005516 engineering process Methods 0.000 claims abstract description 24
- 238000007726 management method Methods 0.000 claims abstract description 24
- 235000013339 cereals Nutrition 0.000 claims abstract description 16
- 238000003745 diagnosis Methods 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 16
- 230000010354 integration Effects 0.000 claims abstract description 14
- 238000013500 data storage Methods 0.000 claims abstract description 6
- 238000010801 machine learning Methods 0.000 claims abstract description 6
- 238000009331 sowing Methods 0.000 claims description 43
- 239000003337 fertilizer Substances 0.000 claims description 16
- 239000002689 soil Substances 0.000 claims description 16
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 12
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- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 8
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 6
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- 238000013526 transfer learning Methods 0.000 claims description 6
- 241000196324 Embryophyta Species 0.000 claims description 5
- 238000013461 design Methods 0.000 claims description 5
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims description 4
- 241000607479 Yersinia pestis Species 0.000 claims description 4
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- 239000008186 active pharmaceutical agent Substances 0.000 claims description 3
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
Abstract
The invention discloses a digital control system for wheat planting in northern arid regions and a use method thereof, wherein the digital control system comprises a data acquisition system, a central processing unit, a three-dimensional data integration platform, a grain crop growth monitoring diagnosis and accurate cultivation service platform and a monitoring platform; the data acquisition system comprises an unmanned plane, remote sensing data and a sensor; the invention establishes a unified big data storage management platform based on data acquisition standards, establishes a high-quality wheat production management holographic system, is oriented to the whole wheat production process, rapidly and intelligently acquires data by utilizing machine learning and data mining technologies, establishes an accurate planting decision model, realizes fine seed selection, accurate fertilization, accurate cultivation, visual management and scientific decision of wheat production, and provides decision basis and technical support for scientific planting and fertilization management, natural disaster early warning and yield monitoring in the wheat production process.
Description
Technical Field
The invention relates to the technical field of agricultural planting, in particular to a digital control system for wheat planting in northern dry areas and a use method thereof.
Background
The wheat has wide production range, strong adaptability and multiple purposes, and is one of the most important grain crops. 35-40% of the population worldwide has wheat as the staple food.
With the continuous reduction of rural labor population, rural people face the problems of what kind of land is going to, how to be good, and the like, and a careful digital technology also needs to be applied.
From the practical situation of the world, modern informatization technologies such as the Internet of things, big data, artificial intelligence and the like can realize accurate management and accurate production, greatly reduce production cost, promote the overall production efficiency of agriculture, realize the overall economic benefit of agriculture and are new ways for realizing high-quality development
Therefore, a digital control system for wheat planting in northern arid regions and a using method thereof become a problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the technical problems that as the labor population of rural areas is continuously reduced, the rural areas face what kind of people go to the planting land, how to be able to plant well and the like.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: the digital control system comprises a data acquisition system, a central processing unit, a three-dimensional data integration platform, a grain crop growth monitoring diagnosis and accurate cultivation service platform and a monitoring platform;
the data acquisition system comprises an unmanned plane, remote sensing data and sensors, wherein the data acquisition system acquires data through the unmanned plane, the remote sensing data and the sensors and sends the acquired data to the central processing unit;
the central processor is used for analyzing and processing the data received from the data acquisition system and transmitting the processed data to the grain crop growth monitoring diagnosis and accurate cultivation service platform;
the three-dimensional data integration platform comprises a decision support library, a basic database and a model knowledge library, adopts a big data storage technology, realizes three-dimensional storage and integration of decision support data, basic data and remote sensing data, and provides powerful data support for a central processor;
the grain crop growth monitoring diagnosis and accurate cultivation service platform comprises, but is not limited to, nutrition diagnosis, variety recommendation, sowing quantity recommendation, agricultural meteorological early warning and information service, disease and pest control and other production management information service, water and fertilizer management, production decision, model configuration and data query and decision, wherein the grain crop growth monitoring diagnosis and accurate cultivation service platform provides data query and decision for the design information service platform, model configuration, production recommendation and decision support, water and fertilizer supply management, growth regulation and control, disaster control information service, and corresponding information service information and control instructions are sent to the monitoring platform through the data transmission system;
the monitoring platform comprises a mobile terminal, a computer terminal and a touch screen, and is used for issuing control commands to corresponding execution units.
Further, the sensors include, but are not limited to, temperature sensors, humidity sensors, light sensors, carbon dioxide concentration sensors, liquid level sensors, and pressure sensors for collecting temperature, light intensity, carbon dioxide concentration, water level, and liquid volume of the planting area.
The application method of the digital control system for wheat planting comprises the following steps of:
step 1, information recommendation service integration: based on the existing wheat production knowledge, a variety knowledge rule base, a sowing-period sowing-amount knowledge base and a fertilization decision knowledge base are established, a variety knowledge base covers a planting area, a proper variety, quality and resistance knowledge base, a recommendation model of the variety before wheat production, fertilization and sowing-period sowing-amount is integrated, and a variety, sowing-period sowing-amount recommendation system based on WebGIS is established;
step 2, intelligent variety recommendation is carried out: because the selection of varieties relates to crop data of plant physiological vernalization time, such as cold degree, sunshine length, weather factors of effective accumulated temperature, soil fertility and soil salinity, which form a multidimensional data space, the variety selection also considers various demands of users on varieties and characteristics of the varieties, and based on a variety recommendation model, a product recommendation decision tree is constructed based on a wheat prenatal basis database;
step 3, performing accurate fertilization recommendation: based on the WebGIS technology, combining with an equilibrium fertilization model, and combining with a Goldmap API to realize fusion of sampling point data, data acquired by a sensor and the WebGIS, so as to display soil nutrient information in real time and provide viewing of organic matter, total nitrogen, effective rate and quick-acting potassium information;
step 4, recommending the sowing period and the sowing quantity: calculating the optimal sowing period and the proper sowing range of the variety, improving the sowing period of the yield, improving the sowing period of the protein content, the number of basic seedlings and the sowing range information according to the conditions of the variety and the wheat region selected by farmers through a sowing period sowing quantity model, and providing reference information of sowing quantity of the sowing period for the user;
step 5, establishing a wheat ecological factor library specification: 30 factors of reliable ecology and easy acquisition are established, and an ecological factor adaptability evaluation and calculation method is established;
step 6, the rapid monitoring of wheat seedling stage groups is realized by using an AR and image processing technology, the identification precision of basic seedlings of wheat in 1-4 leaf stages is more than 95%, the accuracy of AR measurement is more than 98%, and the rapid identification and counting of the basic seedlings of wheat are realized by using the AR and image processing technology;
and 7, developing a rapid identification and counting algorithm of wheat ears in the mature period by utilizing a machine learning and transfer learning technology, wherein the number of the wheat ears identified by the algorithm and the number R2 of the wheat ears counted manually reach more than 0.95, the average time of a single image is 12.17s, the FPS reaches 111, and the rapid identification and counting of the wheat ears are realized by utilizing a smart phone and the transfer learning technology.
Compared with the prior art, the invention has the advantages that: the invention establishes a unified big data storage management platform based on data acquisition standards, establishes a high-quality wheat production management holographic system, is oriented to the whole wheat production process, rapidly and intelligently acquires data by utilizing machine learning and data mining technologies, establishes an accurate planting decision model, develops a management platform which covers the whole life cycle of crops based on soil-crop-environment, realizes fine seed selection, accurate fertilization, accurate cultivation, visual management and scientific decision of wheat production, provides decision basis and technical support for scientific planting and fertilization management, natural disaster early warning and yield monitoring in the wheat production process, has important effects and significance for realizing reasonable and effective utilization of resources, scientific investment, cost saving, synergy, yield increase and quality guarantee, reduces pollution and guaranteeing national grain safety, and is reasonable in design and worth popularizing.
Drawings
FIG. 1 is a block diagram of a digital control system for wheat planting in northern dry-land according to the present invention.
Fig. 2 is a frame diagram of a data acquisition system in a digital control system for wheat planting in northern arid regions according to the present invention.
Fig. 3 is a frame diagram of a three-dimensional data integration platform in the digital control system for wheat planting in northern arid regions.
Fig. 4 is a frame diagram of a monitoring platform in the digital control system for wheat planting in northern dry area of the invention.
FIG. 5 is a model diagram of a variety recommendation in a method of using a digital control system for wheat planting according to the present invention.
FIG. 6 is a model diagram of fertilization recommendations in a method of using a digital control system for wheat planting in accordance with the present invention.
FIG. 7 is a model diagram of a program amount recommendation in a method of using a digital control system for wheat planting according to the present invention.
Detailed Description
The invention relates to a digital control system for wheat planting in northern arid regions and a use method thereof, which are further described in detail below with reference to the accompanying drawings.
The present invention will be described in detail with reference to fig. 1-7.
The digital control system comprises a data acquisition system, a central processing unit, a three-dimensional data integration platform, a grain crop growth monitoring diagnosis and accurate cultivation service platform and a monitoring platform;
the data acquisition system comprises an unmanned plane, remote sensing data and sensors, wherein the data acquisition system acquires data through the unmanned plane, the remote sensing data and the sensors and sends the acquired data to the central processing unit;
the central processor is used for analyzing and processing the data received from the data acquisition system and transmitting the processed data to the grain crop growth monitoring diagnosis and accurate cultivation service platform;
the three-dimensional data integration platform comprises a decision support library, a basic database and a model knowledge library, adopts a big data storage technology, realizes three-dimensional storage and integration of decision support data, basic data and remote sensing data, and provides powerful data support for a central processor;
the grain crop growth monitoring diagnosis and accurate cultivation service platform comprises, but is not limited to, nutrition diagnosis, variety recommendation, sowing quantity recommendation, agricultural meteorological early warning and information service, disease and pest control and other production management information service, water and fertilizer management, production decision, model configuration and data query and decision, wherein the grain crop growth monitoring diagnosis and accurate cultivation service platform provides data query and decision for the design information service platform, model configuration, production recommendation and decision support, water and fertilizer supply management, growth regulation and control, disaster control information service, and corresponding information service information and control instructions are sent to the monitoring platform through the data transmission system;
the monitoring platform comprises a mobile terminal, a computer terminal and a touch screen, and is used for issuing control commands to corresponding execution units.
The sensors include, but are not limited to, temperature sensors, humidity sensors, light sensors, carbon dioxide concentration sensors, liquid level sensors, and pressure sensors for acquiring temperature, carbon dioxide concentration, water level, and liquid volume of the planting area.
The invention relates to a digital control system for wheat planting in northern arid regions and a using method thereof, wherein the implementation process comprises the following steps: step 1, information recommendation service integration: based on the existing wheat production knowledge, a variety knowledge rule base, a sowing-period sowing-amount knowledge base and a fertilization decision knowledge base are established, a variety knowledge base covers a planting area, a proper variety, quality and resistance knowledge base, a recommendation model of the variety before wheat production, fertilization and sowing-period sowing-amount is integrated, and a variety, sowing-period sowing-amount recommendation system based on WebGIS is established;
step 2, intelligent variety recommendation is carried out: because the selection of varieties relates to crop data of plant physiological vernalization time, such as cold degree, sunshine length, weather factors of effective accumulated temperature, soil fertility and soil salinity, which form a multidimensional data space, the variety selection also considers various demands of users on varieties and characteristics of the varieties, and based on a variety recommendation model, a product recommendation decision tree is constructed based on a wheat prenatal basis database;
step 3, performing accurate fertilization recommendation: based on the WebGIS technology, combining with an equilibrium fertilization model, and combining with a Goldmap API to realize fusion of sampling point data, data acquired by a sensor and the WebGIS, so as to display soil nutrient information in real time and provide viewing of organic matter, total nitrogen, effective rate and quick-acting potassium information;
step 4, recommending the sowing period and the sowing quantity: calculating the optimal sowing period and the proper sowing range of the variety, improving the sowing period of the yield, improving the sowing period of the protein content, the number of basic seedlings and the sowing range information according to the conditions of the variety and the wheat region selected by farmers through a sowing period sowing quantity model, and providing reference information of sowing quantity of the sowing period for the user;
step 5, establishing a wheat ecological factor library specification: 30 factors of reliable ecology and easy acquisition are established, and an ecological factor adaptability evaluation and calculation method is established;
step 6, the rapid monitoring of wheat seedling stage groups is realized by using an AR and image processing technology, the identification precision of basic seedlings of wheat in 1-4 leaf stages is more than 95%, the accuracy of AR measurement is more than 98%, and the rapid identification and counting of the basic seedlings of wheat are realized by using the AR and image processing technology;
and 7, developing a rapid identification and counting algorithm of wheat ears in the mature period by utilizing a machine learning and transfer learning technology, wherein the number of the wheat ears identified by the algorithm and the number R2 of the wheat ears counted manually reach more than 0.95, the average time of a single image is 12.17s, the FPS reaches 111, and the rapid identification and counting of the wheat ears are realized by utilizing a smart phone and the transfer learning technology.
The algorithm flow of the variety recommendation is as follows:
1. taking a administrative region where a user is located as basic data, acquiring a suitable region and a wheat region of a variety in a wheat region conditional matching variety database, if a matching result count >1 enters a next matching condition cout=1 to display a matching result, otherwise, ending prompting that no matching result exists;
2. the natural disaster resistant capability of the result set of 1 variety is matched by calling the easy-to-occur natural disaster knowledge of the region from the model knowledge rule base, if the matching result count >1 is entered into the next matching condition cout=1, the matching result is displayed, otherwise, the current variety result set is recommended;
3. the knowledge of the area which is easy to be damaged by diseases and insect pests is called from a model knowledge rule base, the natural resistance of a 2-variety result set is matched, if the matching result count >1 enters the stage 2 multi-objective matching, the cout=1 shows the matching result, otherwise, the current variety result set is recommended;
4. the soil fertility grade of the current region is called from the soil attribute database, and the variety soil fertility grade in the stage 1 variety result set is matched;
5. evaluating the soil production potential of the current region by combining the soil attribute data, the meteorological data and the farmland attribute data, and matching the variety yield potential in the stage 1 variety result set;
6. other targets such as quality, resistance, yield and the like selected by a user are matched with corresponding indexes of varieties in the stage 1 variety result set;
7. and (5) integrating the matching result sets in the steps 4, 5 and 6, solving the intersection set, and taking the variety result set with the intersection set meeting the most targets to recommend to the user.
The fertilization recommendation algorithm flow is as follows:
1. and calculating the fertilizing amount. Calling out a local fertilization model in a fertilization model library, and calculating fertilization data by taking nutrient elements such as nitrogen, phosphorus, potassium and the like in a soil attribute database as model parameters in combination with a local yield target calculated in a variety recommendation process;
2. and (5) recommending fertilization amount. Invoking a local fertilization rule in fertilization knowledge and a fertilization amount suggestion of a recommended variety, comparing the local fertilization rule with the fertilization amount in step 1, and outputting a fertilization amount recommendation result;
3. and (5) recommending fertilization technology. Taking the fertilization technical rule in fertilization knowledge as the basis of fertilization technical recommendation, combining the fertilization technical recommendation of the recommended variety, fusing the fertilization amount, and generating the fertilization technical recommendation;
4. fertilizer recommendation. Matching the fertilizer variety recommended fertilizer variety in the fertilizer database by combining the fertilizer ratio in the fertilizer rule and the fertilizer ratio of the recommended variety;
5. a fertilizer distributor recommends; searching a dealer database according to the recommended fertilizer variety and the fertilizer variety required by the user to recommend the dealer.
The program-broadcasting-amount recommendation algorithm flow is as follows:
1. and (5) determining a broadcasting period range. Calling the production conditions and cultivation system knowledge of the current region in the broadcasting period broadcasting quantity knowledge base to compare and calculate the broadcasting period range of the current region with the proper broadcasting period of the recommended variety;
2. and (5) determining the optimal sowing period. Counting the perennial overwintering period of the current area by using local meteorological data, calculating the accumulated temperature of the overwintering period on the basis, and comparing the accumulated temperature with the optimal sowing period of the recommended variety to determine the optimal sowing date;
3. and (5) determining basic seedlings. Based on the optimal sowing period, calculating proper basic seedlings by combining the local soil fertility, the fertilization level, the spike number of each unit surface and the spike number of each plant;
4. and (5) determining the sowing quantity. And determining the proper sowing quantity according to the basic seedlings and the thousand seed weight, the purity of seeds, the germination rate, the emergence rate and the like of the recommended varieties and the suggested sowing quantity of the varieties.
The invention establishes a unified big data storage management platform based on data acquisition standards, establishes a high-quality wheat production management holographic system, is oriented to the whole wheat production process, rapidly and intelligently acquires data by utilizing machine learning and data mining technologies, establishes an accurate planting decision model, develops a management platform which covers the whole life cycle of crops based on soil-crop-environment, realizes fine seed selection, accurate fertilization, accurate cultivation, visual management and scientific decision of wheat production, provides decision basis and technical support for scientific planting and fertilization management, natural disaster early warning and yield monitoring in the wheat production process, has important effects and significance for realizing reasonable and effective utilization of resources, scientific investment, cost saving, synergy, yield increase and quality guarantee, reduces pollution and guaranteeing national grain safety, and is reasonable in design and worth popularizing.
The invention and its embodiments have been described above with no limitation, and the actual construction is not limited to the embodiments of the invention as shown in the drawings. In summary, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical solution should not be creatively devised without departing from the gist of the present invention.
Claims (3)
1. A digital control system for wheat planting in northern arid regions is characterized in that: the digital control system comprises a data acquisition system, a central processing unit, a three-dimensional data integration platform, a grain crop growth monitoring diagnosis and accurate cultivation service platform and a monitoring platform;
the data acquisition system comprises an unmanned plane, remote sensing data and sensors, wherein the data acquisition system acquires data through the unmanned plane, the remote sensing data and the sensors and sends the acquired data to the central processing unit;
the central processor is used for analyzing and processing the data received from the data acquisition system and transmitting the processed data to the grain crop growth monitoring diagnosis and accurate cultivation service platform;
the three-dimensional data integration platform comprises a decision support library, a basic database and a model knowledge library, adopts a big data storage technology, realizes three-dimensional storage and integration of decision support data, basic data and remote sensing data, and provides powerful data support for a central processor;
the grain crop growth monitoring diagnosis and accurate cultivation service platform comprises, but is not limited to, nutrition diagnosis, variety recommendation, sowing quantity recommendation, agricultural meteorological early warning and information service, disease and pest control and other production management information service, water and fertilizer management, production decision, model configuration and data query and decision, wherein the grain crop growth monitoring diagnosis and accurate cultivation service platform provides data query and decision for the design information service platform, model configuration, production recommendation and decision support, water and fertilizer supply management, growth regulation and control, disaster control information service, and corresponding information service information and control instructions are sent to the monitoring platform through the data transmission system;
the monitoring platform comprises a mobile terminal, a computer terminal and a touch screen, and is used for issuing control commands to corresponding execution units.
2. The digital control system for wheat planting in northern dry areas of claim 1, wherein: the sensors include, but are not limited to, temperature sensors, humidity sensors, light sensors, carbon dioxide concentration sensors, liquid level sensors, and pressure sensors for acquiring temperature, carbon dioxide concentration, water level, and liquid volume of the planting area.
3. A method of using a digital control system for wheat planting comprising the digital control system for northern dry-zone wheat planting of claims 1-2, characterized by: the using method of the digital control system is as follows:
step 1, information recommendation service integration: based on the existing wheat production knowledge, a variety knowledge rule base, a sowing-period sowing-amount knowledge base and a fertilization decision knowledge base are established, a variety knowledge base covers a planting area, a proper variety, quality and resistance knowledge base, a recommendation model of the variety before wheat production, fertilization and sowing-period sowing-amount is integrated, and a variety, sowing-period sowing-amount recommendation system based on WebGIS is established;
step 2, intelligent variety recommendation is carried out: because the selection of varieties relates to crop data of plant physiological vernalization time, such as cold degree, sunshine length, weather factors of effective accumulated temperature, soil fertility and soil salinity, which form a multidimensional data space, the variety selection also considers various demands of users on varieties and characteristics of the varieties, and based on a variety recommendation model, a product recommendation decision tree is constructed based on a wheat prenatal basis database;
step 3, performing accurate fertilization recommendation: based on the WebGIS technology, combining with an equilibrium fertilization model, and combining with a Goldmap API to realize fusion of sampling point data, data acquired by a sensor and the WebGIS, so as to display soil nutrient information in real time and provide viewing of organic matter, total nitrogen, effective rate and quick-acting potassium information;
step 4, recommending the sowing period and the sowing quantity: calculating the optimal sowing period and the proper sowing range of the variety, improving the sowing period of the yield, improving the sowing period of the protein content, the number of basic seedlings and the sowing range information according to the conditions of the variety and the wheat region selected by farmers through a sowing period sowing quantity model, and providing reference information of sowing quantity of the sowing period for the user;
step 5, establishing a wheat ecological factor library specification: 30 factors of reliable ecology and easy acquisition are established, and an ecological factor adaptability evaluation and calculation method is established;
step 6, the rapid monitoring of wheat seedling stage groups is realized by using an AR and image processing technology, the identification precision of basic seedlings of wheat in 1-4 leaf stages is more than 95%, the accuracy of AR measurement is more than 98%, and the rapid identification and counting of the basic seedlings of wheat are realized by using the AR and image processing technology;
and 7, developing a rapid identification and counting algorithm of wheat ears in the mature period by utilizing a machine learning and transfer learning technology, wherein the number of the wheat ears identified by the algorithm and the number R2 of the wheat ears counted manually reach more than 0.95, the average time of a single image is 12.17s, the FPS reaches 111, and the rapid identification and counting of the wheat ears are realized by utilizing a smart phone and the transfer learning technology.
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| CN117391315A (en) * | 2023-12-13 | 2024-01-12 | 杨凌职业技术学院 | Agricultural meteorological data management method and device |
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| CN117391315A (en) * | 2023-12-13 | 2024-01-12 | 杨凌职业技术学院 | Agricultural meteorological data management method and device |
| CN117391315B (en) * | 2023-12-13 | 2024-03-08 | 杨凌职业技术学院 | Agricultural meteorological data management method and device |
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